Abstract
BackgroundIdentification of miRNA-disease associations has attracted much attention recently due to the functional roles of miRNAs implicated in various biological and pathological processes. Great efforts have been made to discover the potential associations between miRNAs and diseases both experimentally and computationally. Although reliable, the experimental methods are in general time-consuming and labor-intensive. In comparison, computational methods are more efficient and applicable to large-scale datasets.MethodsIn this paper, we propose a novel semi-supervised model to predict miRNA-disease associations via ell_{1}-norm graph. Specifically, we first recalculate the miRNA functional similarities as well as the disease semantic similarities based on the latest version of MeSH descriptors and HMDD. We then update the similarity matrices and association matrix iteratively in both miRNA space and disease space. The optimized association matrices from each space are combined together as the final output.ResultsCompared with four state-of-the-art prediction methods, our method achieved favorable performance with AUCs of 0.943 and 0.946 in both global LOOCV and local LOOCV, respectively. In addition, we carried out three types of case studies on five common human diseases, and most of the top 50 predicted miRNAs were confirmed to be associated with the investigated diseases by four databases dbDEMC, PheomiR, miR2Disease and miRwayDB. Specifically, our results provided potential evidence that miRNAs within the same family or cluster were likely to play functional roles together in given diseases.ConclusionsTaken together, the experimental results clearly demonstrated the utility of the proposed method. We anticipated that our method could serve as a reliable and efficient tool for miRNA-disease association prediction.
Highlights
Identification of miRNA-disease associations has attracted much attention recently due to the func‐ tional roles of miRNAs implicated in various biological and pathological processes
We present a novel framework for miRNA-disease association prediction and envision it being a useful tool for future clinical analysis
Performance evaluation To validate the prediction ability of our method, we implemented leave-one-out cross validation (LOOCV) where each known association was left in turn as the test sample and the rest of the known associations were used for optimization
Summary
Identification of miRNA-disease associations has attracted much attention recently due to the func‐ tional roles of miRNAs implicated in various biological and pathological processes. Under the assumption that functionally related miRNAs tend to be involved in phenotypically similar diseases and vice versa, Jiang et al developed the first computational model to prioritize the disease-related miRNAs by constructing a scoring system based on hypergeometric distribution [11] Following their seminal work, Chen et al adopted global network similarities and developed random walk with restart to infer potential miRNA-disease associations [12]. Xuan et al first calculated the miRNA functional similarity by taking miRNA family and cluster information into account, and prioritized disease-related miRNAs in terms of the weighted k most similar neighbors [14] Their method cannot be applied to diseases without any known associated miRNAs. their method cannot be applied to diseases without any known associated miRNAs To solve this issue, they proposed another approach called MIDPE based on bilayer random walk model later on, in which different categories of nodes were assigned different transition weights [15]. The leave-one-out cross validation demonstrated that HGIMDA achieved comparable results
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